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Unsupervised polarimetric SAR urban area classification based on model-based decomposition with cross scattering
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics. National University of Defense Technology, China. (Division of Geoinformatics)
National University of Defense Technology, China.
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
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2016 (English)In: ISPRS journal of photogrammetry and remote sensing (Print), ISSN 0924-2716, E-ISSN 1872-8235, Vol. 116, 86-100 p.Article in journal (Refereed) PublishedText
Abstract [en]

Since it has been validated that cross-polarized scattering (HV) is caused not only by vegetation but also by rotated dihedrals, in this study, we use rotated dihedral corner reflectors to form a cross scattering matrix and propose an extended four-component model-based decomposition method for PolSAR data over urban areas. Unlike other urban area decomposition techniques which need to discriminate the urban and natural areas before decomposition, this proposed method is applied on PolSAR image directly. The building orientation angle is considered in this scattering matrix, making it flexible and adaptive in the decomposition. Therefore, we can separate cross scattering of urban areas from the overall HV component. Further, the cross and helix scattering components are also compared. Then, using these decomposed scattering powers, the buildings and natural areas can be easily discriminated from each other using a simple unsupervised K-means classifier. Moreover, buildings aligned and not aligned along the radar flight direction can be also distinguished clearly. Spaceborne RADARSAT-2 and airborne AIRSAR full polarimetric SAR data are used to validate the performance of our proposed method. The cross scattering power of oriented buildings is generated, leading to a better decomposition result for urban areas with respect to other state-of-the-art urban decomposition techniques. The decomposed scattering powers significantly improve the classification accuracy for urban areas.

Place, publisher, year, edition, pages
Elsevier, 2016. Vol. 116, 86-100 p.
Keyword [en]
Cross scattering matrix, K-means classifier, Model-based decomposition, Polarimetric SAR (PolSAR), Urban area classification, Matrix algebra, Polarimeters, Satellite communication systems, K-means, Model based decompositions, Polarimetric SAR, Scattering matrices, Synthetic aperture radar
National Category
Earth and Related Environmental Sciences Communication Systems
Identifiers
URN: urn:nbn:se:kth:diva-186934DOI: 10.1016/j.isprsjprs.2016.03.009ScopusID: 2-s2.0-84962538521OAI: oai:DiVA.org:kth-186934DiVA: diva2:930589
Note

QC 20160524

Available from: 2016-05-24 Created: 2016-05-16 Last updated: 2016-06-07Bibliographically approved
In thesis
1. Urban Area Information Extraction From Polarimetric SAR Data
Open this publication in new window or tab >>Urban Area Information Extraction From Polarimetric SAR Data
2016 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Polarimetric Synthetic Aperture Radar (PolSAR) has been used for various remote sensing applications since more information could be obtained in multiple polarizations. The overall objective of this thesis is to investigate urban area information extraction from PolSAR data with the following specific objectives: (1) to exploit polarimetric scattering model-based decomposition methods for urban areas, (2) to investigate effective methods for man-made target detection, (3) to develop edge detection and superpixel generation methods, and (4) to investigate urban area classification and segmentation.

Paper 1 proposes a new scattering coherency matrix to model the cross-polarized scattering component from urban areas, which adaptively considers the polarization orientation angles of buildings. Thus, the HV scattering components from forests and oriented urban areas can be modelled respectively. Paper 2 presents two urban area decompositions using this scattering model. After the decomposition, urban scattering components can be effectively extracted.

Paper 3 presents an improved man-made target detection method for PolSAR data based on nonstationarity and asymmetry. Reflection asymmetry was incorporate into the azimuth nonstationarity extraction method to improve the man-made target detection accuracy, i.e., removing the natural areas and detecting the small targets.

In Paper 4, the edge detection of PolSAR data was investigated using SIRV model and Gauss-shaped filter. This detector can locate the edge pixels accurately with fewer omissions. This could be useful for speckle noise reduction, superpixel generation and others.

Paper 5 investigates an unsupervised classification method for PolSAR data in urban areas. The ortho and oriented buildings can be discriminated very well. Paper 6 proposes an adaptive superpixel generation method for PolSAR images. The algorithm produces compact superpixels that can well adhere to image boundaries in both natural and urban areas.

Abstract [sv]

Polarimetriska Synthetic Aperture Radar (PolSAR) har använts för olika fjärranalystillämpningar för, eftersom mer information kan erhållas från multipolarisad data. Det övergripande syftet med denna avhandling är att undersöka informationshämtning över urbana områden från PolSAR data med följande särskilda mål: (1) att utnyttja polarimetrisk spridningsmodellbaserade nedbrytningsmetoder för stadsområden, (2) att undersöka effektiva metoder för upptäckt av konstgjorda objekt, (3) att utveckla metoder som kantavkänning och superpixel generation, och (4) för att undersöka klassificering och segmentering av stadsområden.

Artikel 1 föreslår en ny spridnings-koherens matris för att modellera korspolariserade spridningskomponent från tätorter, som adaptivt utvärderar polariseringsorienteringsvinkel av byggnader. Artikel 2 presenterar nedbrytningstekniken över två urbana områden med hjälp av denna spridningsmodell. Efter nedbrytningen kunde urbana spridningskomponenter effektivt extraheras.

Artikel 3 presenterar en förbättrad detekteringsmetod för konstgjorda mål med PolSAR data baserade på icke-stationaritet och asymmetri. integrerades reflektionsasymmetri i icke-stationaritetsmetoden för att förbättra noggrannheten i upptäckten av konstgjorda föremål, dvs. att ta bort naturområden och upptäcka de små föremålen.

I artikel 4 undersöktes kantdetektering av PolSAR data med hjälp av SIRV modell och ett Gauss-formad filter. Denna detektor kan hitta kantpixlarna noggrant med mindre utelämnande. Detta skulle den vara användbar för reduktion av brus, superpixel generation och andra.

Artikel 5 utforskar en oövervakad klassificeringsmetod av PolSAR data över stadsområden. Orto- och orienterade byggnader kan särskiljas mycket väl. Baserat på artikel 4 föreslår artikel 6 en adaptiv superpixel generationensmetod för PolSAR data. Algoritmen producerar kompakta superpixels som kan kommer att följa bildgränser i både naturliga och stadsområden.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2016. 121 p.
Series
TRITA-SOM, ISSN 1653-6126
Keyword
Polarimetric SAR, Scattering Decomposition, Man-Made Target Detection, Edge Detection, Superpixel, Urban Classification, Polarimetrisk SAR, Spridningsnedbrytning, Upptäckt av artificiella objekt, Kantupptäckt, Superpixel, Urban klassificering
National Category
Remote Sensing
Research subject
Geodesy and Geoinformatics
Identifiers
urn:nbn:se:kth:diva-187951 (URN)978-91-7729-047-6 (ISBN)
Public defence
2016-08-25, Kollegiesalen, Brinellvägen 8, KTH-Campus, Stockholm, 13:30 (English)
Opponent
Supervisors
Note

QC 20160607

Available from: 2016-06-07 Created: 2016-06-01 Last updated: 2016-06-07Bibliographically approved

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